In harmony: inquiry based learning in a blended physics and music class
Bibliographic record
Abstract
Abstract The power of music to resonate within us transcends conventional boundaries established in cultural, geographic, and political contexts. In our world, as physics educators, so does the resonating of physics phenomena. Secondary level physics is a perfect place to blend these two genres. While advocating for STEM-based education is at the forefront of pedagogical reform, seldom do we use this cross-boundary vision as the foundation to teach and learn in true collaboration of science and arts classrooms. As music enthusiasts, and physics educators, we developed new resources for a blended music and physics class through inquiry-based learning activities. Punctuated with modern technology, we aimed our activities for an engaging learning experience towards developing conceptual understandings of sound and harmonics at the grade 11 level. The umbrella activity shared here was designed to engage a wide range of students through the universal language of music, and provide them a hands-on and minds-on experience to explore harmonics through both music and physics lenses. It is our intention to provide readers with an overview of the activity, a description of exemplar student-designed inquiry-based investigations, and helpful suggestions for potential for use in reader’s classrooms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".